Reinforcement learning

Policy optimisation, reward modelling and RLHF — how models are trained by feedback rather than by labels.

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arXiv AI

Jul 14

Average-Power-Budgeted Underwater Vehicle Control via Constrained Reinforcement Learning

arXiv:2606. 25680v2 Announce Type: replace-cross Abstract: Underwater vehicles operate from a fixed onboard energy budget that propulsion rapidly depletes, so a controller that completes its task while drawing less thruster power directly extends mission range and endurance.

By Yinuo Wang, Gavin Tao, Yuze Liu, John V. Ringwood

reinforcement-learning

arXiv AI

Jul 14

Length Penalties Make Chain-of-Thought Less Monitorable

arXiv:2607. 09786v1 Announce Type: new Abstract: Length-penalized reinforcement learning can shorten chain-of-thought reasoning while hiding an influence that drives the model's answer.

By Bryce Little

reinforcement-learning benchmarks

arXiv AI

Jul 14

SCALECUA: Scaling Computer Use Agents with Verifiable Task Synthesis and Efficient Online RL

arXiv:2607. 11185v1 Announce Type: new Abstract: Computer use agents (CUAs) are emerging as a powerful interface for automating complex digital workflows through visual perception and GUI execution.

By Bowen Lv, Xiao Liu, Yanyu Ren, Hanyu Lai, Bohao Jing, Hanchen Zhang, Yanxiao Zhao, Shuntian Yao, Jie Tang, Yuxiao Dong

agents computer-vision reinforcement-learning benchmarks

arXiv AI

Jul 14

Auditing the Risk Claims of Distributional Reinforcement Learning

arXiv:2607. 11607v1 Announce Type: new Abstract: Distributional reinforcement learning agents learn full return distributions that are increasingly read at face value: for interpretability, risk-sensitive control, and safety monitoring.

By Hari Prasad

agents reinforcement-learning benchmarks safety

arXiv AI

Jul 14

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization

arXiv:2607. 10127v1 Announce Type: cross Abstract: Evolutionary program search guided by Large Language Models (LLMs) has emerged as a powerful paradigm for automated scientific discovery.

By Xuanzhou Chen, Taoli Cheng

llms rag reinforcement-learning fine-tuning benchmarks

arXiv AI

Jul 14

World Models as Adversaries: Multi-Agent Self-Play Fine-Tuning for Robust Motion Planning

arXiv:2607. 10630v1 Announce Type: cross Abstract: Robust motion planning in dense traffic requires autonomous vehicles to interact in rare and safety-critical scenarios that are underrepresented in naturalistic driving data.

By Tong Nie, Yuewen Mei, Junlin He, Yihong Tang, Jian Sun, Wei Ma

agents reinforcement-learning fine-tuning benchmarks safety

arXiv AI

Jul 14

Efficient Online Proportional Sampling with Applications to Smoothed Online Learning

arXiv:2607. 10963v1 Announce Type: cross Abstract: We study the problem of efficient online proportional sampling from a high-dimensional domain under a $\sigma$-smoothed adversary, where the sampling distribution is induced by a dynamically evolving weight function defined over a sequence of piecewise-structured partitions.

By Amirmahdi Mirfakhar, Maria-Florina Balcan, Hedyeh Beyhaghi

agents reinforcement-learning

arXiv AI

Jul 14

FAST: A Framework for Aligned Sampling and Training in Parallel Reinforcement Learning for Autonomous Driving

arXiv:2606. 21587v2 Announce Type: replace-cross Abstract: Deep reinforcement learning is pivotal for closed-loop autonomous driving yet remains constrained by severe bottlenecks in sampling efficiency.

By Bonan Wang, Letian Tao, Bin Shuai, Jiaxin Gao, Wenxin Zhao, Wei Xiong, Kehua Sheng, Bo Zhang, Yang Guan, Shengbo Eben Li

agents reinforcement-learning robotics safety

arXiv AI

Jul 14

DiffEEG: A Self-Supervised Denoising Diffusion Model for Learning EEG Generic Representations

arXiv:2607. 11578v1 Announce Type: cross Abstract: Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events comprise less than 10\% of clinical recordings.

By Abdulkader Helwan, Lina Abou-Abbas, Hussein El Amouri, Belkacem Chikhaoui, Khadidja Henni

diffusion reinforcement-learning fine-tuning

arXiv AI

Jul 14

SVR-R1: Bootstrapping Multi-modal Reasoning with Self-verification in Reinforcement Learning

arXiv:2607. 10966v1 Announce Type: new Abstract: We introduce Self-Verified Reasoner (SVR-R1), a multi-turn RL framework that turns a model's own verification into a learning signal for multimodal reasoning.

By Mingyuan Wu, Jingcheng Yang, Shengyi Qian, Xudong Wang, Jize Jiang, Qifan Wang, Aashu Singh, Khoi Pham, Fei Liu, Zhaolun Su, Zhuokai Zhao, Klara Nahrstedt, Jianyu Wang, Hanchao Yu

reinforcement-learning multimodal benchmarks

arXiv Machine Learning

Jul 14

Generalizing Preference-based Reinforcement Learning: a Rationality Model for Incomparability

arXiv:2607. 11432v1 Announce Type: new Abstract: In this work, we study the reinforcement learning (RL) problem from pairwise trajectory comparisons provided by a human expert.

By Simone Drago, Marco Mussi, Leonardo Bianconi, Alberto Maria Metelli

reinforcement-learning

arXiv Machine Learning

Jul 14

Link Adaptation Using Joint-Thompson Sampling

arXiv:2607. 11075v1 Announce Type: new Abstract: The choice of Modulation and Coding (MCS) type for a particular channel condition is made through link adaptation (LA) algorithms that operate at the MAC layer.

By Vignatha Vinjam, Manjunath Kolavennu, Myna Vajha, Karthik Periyapattana Narayanaprasad

reinforcement-learning efficiency

arXiv Machine Learning

Jul 14

Predictive Divergence Masks for LLM RL

arXiv:2607. 10848v1 Announce Type: new Abstract: Reinforcement learning for large language models (LLMs) typically relies on trust-region masks to stabilize off-policy updates.

By Xiangxin Zhou, Jiarui Yao, Penghui Qi, Bowen Ping, Jiaqi Tang, Haonan Wang, Tianyu Pang

llms reinforcement-learning

arXiv AI

Jul 14

STAMP: Provenance-Guided Credit Assignment for Deep Search Agents

arXiv:2607. 11172v1 Announce Type: new Abstract: Reinforcement learning for deep-search agents has largely focused on trajectory-level scoring -- outcome correctness, citation-aware rewards, and evidence coverage.

By Ke Xu, Han Xu, Xinran Chen, Yuqian Wang, Zhixuan Li, Xiaojian Liu, Changwo Wu, Jianqiang Xia, Yuchen Li

agents reinforcement-learning fine-tuning

arXiv AI

Jul 14

Behavioural Signatures of Risk-Sensitive Decision-Making in Large Language Models

arXiv:2607. 10251v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly used in decision support, it is important to understand whether their choices under uncertainty exhibit stable and interpretable behavioural regularities.

By Xuankun Rong, Wenke Huang, Bo Du, Dacheng Tao, Mang Ye

llms reinforcement-learning

arXiv AI

Jul 14

Comparing Socially-Equitable Renewable Energy Budget Allocation MDP Policies in Mature and Emerging Economies

arXiv:2607. 10201v1 Announce Type: cross Abstract: Equitable renewable-energy planning is a sequential decision problem, but the decision variables available to a public planner differ sharply between mature and emerging economies.

By Riya Kinnarkar, Mansur M. Arief, Yan Pratama Akhra, Dino Arla

reinforcement-learning

arXiv Machine Learning

Jul 14

Oracle-Efficient Combinatorial Semi-Bandits

arXiv:2510. 21431v2 Announce Type: replace-cross Abstract: We study the combinatorial semi-bandit problem where an agent selects a subset of base arms and receives individual feedback.

By Jung-hun Kim, Milan Vojnovi\'c, Min-hwan Oh

agents reinforcement-learning

arXiv Machine Learning

Jul 14

Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems

arXiv:2603. 05559v2 Announce Type: replace Abstract: Decision makers exploiting photonic chaotic dynamics obtained by semiconductor lasers provide an ultrafast approach to solving multi-armed bandit problems by using a temporal optical signal as the driving source for sequential decisions.

By Tomoki Yamagami, Mikio Hasegawa, Takatomo Mihana, Ryoichi Horisaki, Atsushi Uchida

reinforcement-learning robotics

arXiv AI

Jul 14

CRINN: Contrastive Reinforcement Learning for Approximate Nearest Neighbor Search

arXiv:2508. 02091v3 Announce Type: replace-cross Abstract: Approximate nearest-neighbor search (ANNS) algorithms have become increasingly critical for recent AI applications, particularly in retrieval-augmented generation (RAG) and agent-based LLM applications.

By Xiaoya Li, Albert Wang, Guoyin Wang, Chris Shum, Jiwei Li

llms rag agents reinforcement-learning benchmarks

arXiv AI

Jul 14

Learning Residual Kinematic Corrections for Continuous Neural Decoding via Reinforcement Learning

arXiv:2607. 11530v1 Announce Type: new Abstract: Decoding continuous three-dimensional (3D) motor imagery (MI) using non-invasive electroencephalography (EEG)-based brain--computer interfaces (BCIs) remains challenging due to signal variability and residual decoding errors.

By Jiamian Li, Niall McShane, Attila Korik, Naomi du Bois, Karl McCreadie, Leen Jabban, Benjamin Metcalfe, \"Ozg\"ur \c{S}im\c{s}ek, Damien Coyle

agents reinforcement-learning

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